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| 1 | COMBINING CLASSIFIERS FOR CREDIT RISK PREDICTION显示文摘Credit risk prediction models seek to predict quality factors such as whether an individual will default (bad applicant) on a loan or not (good applicant). This can be treated as a kind of machine learning (ML) problem. Recently, the use of ML algorithms has proven to be of great practical value in solving a variety of risk problems including credit risk prediction. One of the most active areas of recent research in ML has been the use of ensemble (combining) classifiers. Research indicates that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular class. This paper explores the predicted behaviour of five classifiers for different types of noise in terms of credit risk prediction accuracy, and how could such accuracy be improved by using pairs of classifier ensembles. Benchmarking results on five credit datasets and comparison with the performance of each individual classifier on predictive accuracy at various attribute noise levels are presented. The experimental evaluation shows that the ensemble of classifiers technique has the potential to improve prediction accuracy. | Bhekisipho TWALA | 2009 | Journal of Systems Science and Systems Engineering2009，18，3： | 2 |
| 2 | Multiple classifier application to credit risk assessment 显示文摘 | Twala B | 2010 | Expert Systems with Applications2010，37，4： | 1 |
| 3 | Organizational adaptation to complexity: a study of the south african insurance market as a complex adaptive system through statistical risk analysis显示文摘 | Paul S Twala B Marwala T | 2012 | Systems engineering Procedia2012，4，： | 1 |
| 4 | Partial imputation of un- seen records to improve classification using a hybrid multi-la- yered artificial immune system and genetic algorithm 显示文摘 | Duma M Marwala T Twala B | 2013 | Applied Soft Computing2013，13，12： | 1 |
| 5 | A Perivascular Niche for Brain Tumor Stem Cells显示文摘 | Christopher Calabrese Helen Poppleton Mehmet Kocak Twala L. Hogg Christine Fuller Blair Hamner Eun Young Oh M. Waleed Gaber David Finklestein Meredith Allen Adrian Frank Ildar T. Bayazitov Stanislav S. Zakharenko Amar Gajjar Andrew Davidoff Richard J. Gil | 2007 | Cancer Cell2007，，1： | 1 |
| 6 | Multiple classifier application to credit risk assessment显示文摘 | TWALA B | 2010 | Expert Systems with Applications2010，，37： | 1 |
| 7 | Good Methods for Coping with Missing Data in Decision Trees显示文摘 | Twala B Jones M C Hand D J | 2008 | Pattern Recognition Letters2008，29，7： | 1 |
| 8 | Nelwamondo, Tshilidzi Marwala, 2012, Par- tial Imputation to Improve Predictive Modelling in Insurance Risk Classification Using a Hybrid PositiveSelection Algorithm and Correlation-based Feature Selection 显示文摘 | Mlungisi Duma Bhekisipho Twala Fulufhelo V | 2012 | Current Science2012，，： | 1 |
| 9 | Optimal integration of solar home systems and appliance scheduling for residential homes under severe national load shedding显示文摘In developing countries like South Africa,users experienced more than 1030 hours of load shedding outages in just the first half of 2023 due to inadequate power supply from the national grid.Residential homes that cannot afford to take actions to mitigate the challenges of load shedding are severely inconvenienced as they have to reschedule their demand involuntarily.This study presents optimal strategies to guide households in determining suitable scheduling and sizing solutions for solar home systems to mitigate the inconvenience experienced by residents due to load shedding.To start with,we predict the load shedding stages that are used as input for the optimal strategies by using the K-Nearest Neighbour(KNN)algorithm.Based on an accurate forecast of the future load shedding patterns,we formulate the residents’inconvenience and the loss of power supply probability during load shedding as the objective function.When solving the multi-objective optimisation problem,four different strategies to fight against load shedding are identified,namely(1)optimal home appliance scheduling(HAS)under load shedding;(2)optimal HAS supported by solar panels;(3)optimal HAS supported by batteries,and(4)optimal HAS supported by the solar home system with both solar panels and batteries.Among these strategies,appliance scheduling with an optimally sized 9.6 kWh battery and a 2.74 kWp panel array of five 550 Wp panels,eliminates the loss of power supply probability and reduces the inconvenience by 92%when tested under the South African load shedding cases in 2023. | Sakhile Twala Xianming Ye Xiaohua Xia Lijun Zhang | 2023 | Journal of Automation and Intelligence2023，2，4： | 0 |
| 10 | State-Based Offloading Model for Improving Response Rate of IoT Services显示文摘The Internet of Things(IoT)is a heterogeneous information sharing and access platform that provides services in a pervasive manner.Task and computation offloading in the IoT helps to improve the response rate and the availability of resources.Task offloading in a service-centric IoT environment mitigates the complexity in response delivery and request processing.In this paper,the state-based task offloading method(STOM)is introduced with a view to maximize the service response rate and reduce the response time of the varying request densities.The proposed method is designed using the Markov decision-making model to improve the rate of requests processed.By defining optimal states and filtering the actions based on the probability of response and request analysis,this method achieves less response time.Based on the defined states,request processing and resource allocations are performed to reduce the backlogs in handling multiple requests.The proposed method is verified for the response rate and time for the varying requests and processing servers through an experimental analysis.From the experimental analysis,the proposed method is found to improve response rate and reduce backlogs,response time,and offloading factor by 11.5%,20.19%,20.31%,and 8.85%,respectively. | K.Sakthidasan Bhekisipho Twala S.Yuvaraj K.Vijayan S.Praveenkumar Prashant Mani C.Bharatiraja | 2021 | Computers, Materials & Continua2021，，6： | 0 |
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